Online surface activation treatment method for bidirectional stretch PET high light film

Abstract

The application discloses an online surface activation treatment method for bidirectional stretching PET high light film, and relates to the technical field of plastic processing. The method comprises the following steps: collecting parameter-oriented sets and corresponding activation defect multi-dimensional fitting functions to obtain an activation defect model; taking a characteristic parameter whose actual value exceeds a corresponding normal range as a target characteristic parameter; obtaining a suspected activation defect type; forming a to-be-verified activation defect combination; decomposing the actual value of the target characteristic parameter; calculating a gap coefficient of the to-be-verified activation defect combination; and processing the PET high light film according to the abnormal degree of the actual activation defect type. The parameter-oriented sets of the activation defect type, the activation defect multi-dimensional fitting functions of the parameter-oriented sets and the gap coefficient of the to-be-verified activation defect combination are obtained, the activation defect condition most consistent with the actual condition is selected from various conditions, and thus the type and abnormal degree of the activation defect are determined.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of plastic processing, in particular to an online surface activation treatment method for bidirectional stretching PET high-gloss film. BACKGROUND

[0002] The high-gloss film is a film composite product with glossiness exceeding 100, which is mainly applied to the fields of building materials and home decoration. The structure of the high-gloss film is composed of a PET film, a surface treatment agent, ink, an adhesive and a PVC film. The surface activation treatment of the PET high-gloss film mainly improves the adhesion and surface performance through a surface treatment process. Different treatment methods with different parameters exist according to the different surface conditions of the high-gloss film. The high-gloss film with a surface meeting the requirements does not need to be treated, and the high-gloss film with a surface not meeting the requirements needs to be treated according to the actual situation.

[0003] The surface activation condition of the high-gloss film is influenced by multiple parameters in parallel, and multiple activation defects with different degrees of abnormality may exist in parallel, so it is difficult to determine the accurate condition of the activation defects, and targeted treatment cannot be performed. SUMMARY

[0004] To solve the above technical problems, the application provides an online surface activation treatment method for bidirectional stretching PET high-gloss film, which solves the problems in the background art.

[0005] To achieve the above purposes, the application adopts the following technical scheme:

[0006] An online surface activation treatment method for bidirectional stretching PET high-gloss film comprises the following steps:

[0007] At least one activation defect type is obtained, each activation defect type contains only one activation defect reason, and a treatment scheme with an abnormal degree of the activation defect type as a reference value is obtained in advance;

[0008] At least one characteristic parameter of the activation condition of the PET high-gloss film is obtained, and a normal range of the characteristic parameter when the activation condition of the PET high-gloss film is normal is obtained;

[0009] A parameter-oriented set of the activation defect type is constructed, the parameter-oriented set is composed of the characteristic parameter, a multi-dimensional fitting function of the activation defect is formed for the parameter-oriented set, and an activation defect model is obtained by summarizing the parameter-oriented set and the corresponding multi-dimensional fitting function of the activation defect;

[0010] The actual activation condition of the surface of the PET high-gloss film is inspected to obtain actual values of the characteristic parameters;

[0011] The characteristic parameter with an actual value exceeding the corresponding normal range is taken as a target characteristic parameter;

[0012] Based on the target characterization parameters and parameter-guided set, at least one suspected activation defect type is obtained;

[0013] Based on the suspected activation defect type, at least one combination of activation defects to be verified is formed;

[0014] Based on the suspected activation defect types in the activation defect combination to be verified, the actual values ​​of the target characterization parameters are decomposed.

[0015] Based on the decomposition results of the actual values ​​of the target characterization parameters, the gap coefficient of the activation defect combination to be verified is calculated.

[0016] The activation defect type in the combination of activation defects to be verified with the smallest difference coefficient is taken as the actual activation defect type, and the PET high-gloss film is processed according to the degree of abnormality of the actual activation defect type.

[0017] Preferably, the construction of the parameter-guided set for activation defect types includes the following steps:

[0018] Each time an activation defect type occurs, the characterization parameters that exceed the normal range of the characterization parameters are summarized into a first guide preparatory set.

[0019] When the activation defect type does not appear, the characterization parameters that are outside the normal range of the characterization parameters are summarized into the second guide preparatory set;

[0020] By taking the intersection of at least one first pre-guiding set, a preliminary guiding set is obtained;

[0021] The parameterized set is obtained by taking the difference between the initial guiding set and the second guiding preparatory set.

[0022] Preferably, the activation defect multidimensional fitting function for forming the parameter-guided set includes the following steps:

[0023] Based on historical detection data, the range of values ​​of the characterization parameters in the parameter-guided set that are outside the normal range is obtained. The range of values ​​of the characterization parameters in the parameter-guided set is divided into equal intervals to obtain at least one identification point. The identification point corresponds to the characterization parameter.

[0024] The representation parameters are randomly selected from the corresponding recognition points to form the first assignment scheme of the parameter-guided set;

[0025] The characterization parameters are all taken as fixed values ​​corresponding to the identification points, forming a second assignment scheme for the parameter-guided set;

[0026] The representation parameter is the sum of the values ​​of the representation parameter in the first assignment scheme and the values ​​of the representation parameter in the second assignment scheme, forming the third assignment scheme;

[0027] Under the condition that the parameter-guided set adopts the third assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the first anomaly degree;

[0028] Under the condition that the parameter-guided set adopts the second assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the second anomaly degree;

[0029] The third degree of abnormality is obtained by subtracting the first degree of abnormality from the second degree of abnormality.

[0030] The values ​​of the characterization parameters in the first assignment scheme are paired and fitted with the third degree of anomaly to obtain a multidimensional fitting function for activation defects, where the values ​​of the characterization parameters in the first assignment scheme are independent variables and the third degree of anomaly is the dependent variable.

[0031] Preferably, obtaining at least one suspected activation defect type based on the target characterization parameters and parameter-guided set includes the following steps:

[0032] If all the representation parameters in the parameter-guided set are target representation parameters, then the parameter-guided set is taken as the target parameter-guided set; otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-guided set is taken as the suspected activation defect type.

[0033] Preferably, forming at least one combination of activation defects to be verified based on the suspected activation defect type includes the following steps:

[0034] Obtain all possible combinations of at least one suspected activation defect type, and treat them as combinations of suspected activation defect types.

[0035] The set of parameters corresponding to the suspected activation defect types in the suspected activation defect type combination is combined and then the set of guidance targets is obtained by taking the union of the sets of parameters.

[0036] When the set of guiding targets contains all the target characterization parameters, the suspected activation defect type combination is taken as the activation defect combination to be verified; otherwise, no processing is performed.

[0037] Preferably, the decomposition of the actual values ​​of the target characterization parameters based on the suspected activation defect types in the activation defect combination to be verified includes the following steps:

[0038] The portion of the actual value of the target characterization parameter that exceeds the normal range is used as the feature value;

[0039] Obtain the anomaly range of the suspected activation defect type, divide the anomaly range of the suspected activation defect type at equal intervals, and obtain at least one test point;

[0040] The abnormality of suspected activation defect types is randomly selected from test points, and each selection method is used as an abnormal selection scheme for the combination of activation defects to be verified.

[0041] A proportionality coefficient is formed for suspected activation defect types, and based on the proportionality coefficient, a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is formed;

[0042] The degree of anomalousness of the suspected activation defect type in the eigenvalue and anomalous value scheme is multiplied by the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter to obtain the excess characterization value of the target characterization parameter of the suspected activation defect type. The excess characterization value of the target characterization parameter corresponds to the anomalous value scheme.

[0043] Preferably, the proportion coefficient for forming suspected activation defect types includes the following steps:

[0044] The intersection of the abnormal ranges of all suspected activation defect types is taken to obtain the feature range, and at least one feature point is uniformly selected in the feature range;

[0045] The values ​​of the feature points are substituted into the multidimensional fitting function of the activation defect corresponding to the suspected activation defect type, which serves as the constraint condition for the parameter-guided set corresponding to the suspected activation defect type.

[0046] All suspected activation defect types other than those whose proportional coefficients are to be determined are denoted as characteristic suspected activation defect types.

[0047] By combining the constraint conditions for suspected activation defect types with the constraint conditions for characteristic suspected activation defect types, at least one set of constraint conditions for suspected activation defect types can be obtained.

[0048] Under the simultaneous constraints of suspected activation defect types, the characterization parameters in the parameter-guided set corresponding to the suspected activation defect types are integrated to obtain the first integral value;

[0049] The second integral value is obtained by summing at least one first integral value for at least one feature point;

[0050] The second integral values ​​of all suspected activation defect types are summed to obtain the third integral value;

[0051] By comparing the second integral value with the third integral value, a proportionality coefficient for suspected activation defect types can be obtained.

[0052] Preferably, the process of forming the decomposition coefficients of the suspected activation defect type relative to the target characterization parameter includes the following steps:

[0053] When the parameter-guided set of the suspected activation defect type contains the target characterization parameter, the suspected activation defect type is taken as the target suspected activation defect type. The proportion coefficient of the target suspected activation defect type is accumulated to obtain the comprehensive coefficient. The proportion coefficient of the target suspected activation defect type is divided by the comprehensive coefficient to obtain the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter.

[0054] When the parameter-guided set of the suspected activation defect type does not contain the target characterization parameter, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

[0055] Preferably, calculating the gap coefficient of the activation defect combination to be verified based on the decomposition results of the actual values ​​of the target characterization parameters includes the following steps:

[0056] Substitute the excess values ​​of the target characterization parameters of the suspected activation defect type in the anomaly sampling scheme into the corresponding activation defect multidimensional fitting function to obtain the predicted anomaly degree of the suspected activation defect type.

[0057] The variance between the abnormality degree of the suspected activation defect type and the predicted abnormality degree of the suspected activation defect type in the abnormal value scheme is calculated to obtain the deviation coefficient of the abnormal value scheme of the activation defect combination to be verified.

[0058] The minimum deviation coefficient of the abnormal value scheme of the activation defect combination to be verified is taken as the difference coefficient of the activation defect combination to be verified.

[0059] Preferably, the process of treating the PET high-gloss film according to the degree of abnormality of the actual activation defect type includes the following steps:

[0060] The combination of activation defects to be verified that produces the actual activation defect type is taken as the target activation defect combination to be verified, and the abnormal value scheme of the target activation defect combination with the smallest deviation coefficient is taken as the target abnormal value scheme.

[0061] The value of the degree of abnormality of the actual activation defect type in the target abnormal value scheme is taken as the degree of abnormality of the actual activation defect type.

[0062] The abnormality of the actual activation defect type is divided by the baseline value to obtain the treatment coefficient. The treatment coefficient is multiplied by the parameters in the treatment scheme corresponding to the actual activation defect type and then summed to obtain the treatment correction scheme.

[0063] The treatment and correction schemes for all actual activation defect types are summarized to obtain the actual treatment scheme, and the PET high-gloss film is treated according to the actual treatment scheme.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] By constructing a parameter-guided set of activation defect types, forming a multidimensional fitting function for activation defects in the parameter-guided set, decomposing the actual values ​​of the target characterization parameters, and calculating the difference coefficient of the combination of activation defects to be verified, the suspected activation defect types are determined based on the actual values ​​of the obtained characterization parameters. Based on this, the activation defect situation is predicted. By comparing the predictions of various situations, the activation defect situation that best matches the actual situation is selected from various situations. Thus, both the type and degree of activation defect are determined. Therefore, targeted activation treatment can be carried out based on the detection results of activation defects. Attached Figure Description

[0066] Figure 1 This is a schematic flowchart of the online surface activation treatment method for biaxially oriented PET high-gloss film of the present invention;

[0067] Figure 2 This is a schematic diagram illustrating the process of constructing a parameter-guided set of activation defect types according to the present invention;

[0068] Figure 3 This is a schematic diagram of the process for forming a parameter-guided set of the activation defect multidimensional fitting function according to the present invention;

[0069] Figure 4 This is a schematic diagram of the process of forming at least one combination of activation defects to be verified based on the suspected activation defect type of the present invention.

[0070] Figure 5 This is a schematic diagram of the process of decomposing the actual values ​​of the target characterization parameters based on the suspected activation defect types in the activation defect combination to be verified according to the present invention.

[0071] Figure 6 This is a schematic diagram of the process for forming the proportional coefficient of suspected activation defect type according to the present invention;

[0072] Figure 7 This is a schematic diagram of the process for forming the decomposition coefficients of the suspected activation defect type relative to the target characterization parameter according to the present invention;

[0073] Figure 8 This is a flowchart illustrating the process of calculating the gap coefficient of the activated defect combination to be verified based on the decomposition results of the actual numerical values ​​of the target characterization parameters according to the present invention.

[0074] Figure 9 This is a schematic diagram of the process of processing PET high-gloss film according to the degree of abnormality of the actual activation defect type according to the present invention. Detailed Implementation

[0075] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0076] Reference Figure 1 As shown, an online surface activation treatment method for biaxially oriented PET high-gloss film includes:

[0077] Obtain at least one activation defect type, each activation defect type contains only one activation defect cause, and pre-obtain a processing plan based on the abnormality level of the activation defect type as a baseline value;

[0078] Obtain at least one characterization parameter of the activation status of the PET high-gloss film, and obtain the normal range of the characterization parameter when the activation status of the PET high-gloss film is normal;

[0079] A parameter-guided set of activation defect types is constructed. The parameter-guided set consists of characterization parameters, forming a multidimensional fitting function for activation defects of the parameter-guided set. The activation defect model is obtained by summarizing the parameter-guided set and its corresponding multidimensional fitting function for activation defects.

[0080] The actual activation status of the PET high-gloss film surface was inspected to obtain the actual values ​​of the characterization parameters.

[0081] The representation parameter whose actual value exceeds the corresponding normal range is used as the target representation parameter;

[0082] Based on the target characterization parameters and parameter-guided set, at least one suspected activation defect type is obtained;

[0083] Based on the suspected activation defect type, at least one combination of activation defects to be verified is formed;

[0084] Based on the suspected activation defect types in the activation defect combination to be verified, the actual values ​​of the target characterization parameters are decomposed.

[0085] Based on the decomposition results of the actual values ​​of the target characterization parameters, the gap coefficient of the activation defect combination to be verified is calculated.

[0086] The activation defect type in the combination of activation defects to be verified with the smallest difference coefficient is taken as the actual activation defect type, and the PET high-gloss film is processed according to the degree of abnormality of the actual activation defect type.

[0087] In this scheme, it is necessary to determine the type of activation defect and the degree of abnormality of the activation defect type when multiple activation defects occur in parallel and the degree of abnormality of the activation defects is unknown. However, since both the type of activation defect and the degree of abnormality are unknown, it is necessary to first generate possible combinations, predict each combination, and then compare them to obtain the combination that best matches the actual situation.

[0088] Surface activation treatments typically include corona treatment, coating, and flame treatment, as well as other methods. Different surface activation requirements necessitate different processing parameter settings. Therefore, it is advisable to pre-set activation defect types, as each type has a relatively small parameter range. Thus, when identifying actual activation defect types, using a predetermined processing scheme as a benchmark results in acceptable errors.

[0089] Activation defects usually do not refer to manufacturing defects in the film itself, but rather to the failure of the hot melt adhesive layer or coating of the high-gloss film to reach the ideal melt bonding state due to improper setting of process parameters during the lamination process. Since PET high-gloss film is laminated on various substrates, the lamination parameters need to be changed accordingly for different substrates; otherwise, activation defects are likely to occur.

[0090] Activation defects include poor adhesion, poor gloss, white spots, and bubbles or orange peel texture. The presence of bubbles or orange peel texture will result in insufficient smoothness.

[0091] The reasons for poor adhesion are as follows:

[0092] 1. Heating temperature is too low: The heating temperature does not reach the melting point of the adhesive layer, so it cannot melt and flow fully;

[0093] 2. Lamination speed is too fast: The film passes under the hot roller for too short a time, and the heat does not have enough time to be transferred to the adhesive layer;

[0094] 3. Insufficient pressure: Even if the adhesive melts, insufficient pressure will prevent it from fully adhering to and penetrating the printed material;

[0095] The reasons for poor gloss are as follows:

[0096] 1. Heating temperature is too low: the adhesive layer failed to completely melt and form a smooth mirror surface;

[0097] 2. Insufficient pressure: This also leads to insufficient leveling of the adhesive layer;

[0098] 3. Poor membrane quality: Problems with the adhesive layer or coating formulation, resulting in a narrow activation window;

[0099] The presence of white spots is due to the same reason as poor adhesion.

[0100] The reasons for the presence of air bubbles or orange peel texture are as follows:

[0101] 1. Excessive heating temperature: This can cause the adhesive layer to melt excessively or even decompose, producing gas, or causing the paper moisture to evaporate rapidly;

[0102] 2. Uneven heating: The surface temperature of the hot roller is inconsistent, and some areas are over-activated;

[0103] 3. Too slow: The residence time at high temperature is too long, resulting in over-activation;

[0104] In order to identify all activation defect types, it is necessary to determine the characterization parameters for each activation defect type, as follows:

[0105] First, the characterization parameters used in the testing of PET high-gloss film are determined, namely dyne value, gloss, haze, transmittance, surface tension, surface hardness, thickness, elastic modulus, white content, and transparent outline content under the condition of an incident angle of 20°.

[0106] When comparing data from various activation defect types, the main characterization parameters for each type can be identified, as follows:

[0107] The characteristic parameter for poor adhesion is the dyne value, which is tested using a dyne pen and dyne liquid kit. The lower limit of the dyne value is set differently depending on the application of the PET high-gloss film. Usually, the lower limit of the dyne value is set between 40-42 mN / m. For some special applications (such as automotive window tinting), the lower limit of the dyne value may be set to 44 mN / m or higher.

[0108] The characterization parameter for poor gloss is the gloss under the condition of an incident angle of 20°. The lower limit of gloss is 85 GU. Gloss is measured using a gloss meter.

[0109] The characteristic parameter for the presence of white spots is the white percentage, which is the area percentage of white pixels identified through image recognition. This percentage has a lower limit, which can be set by empirical data.

[0110] The characteristic parameter for the presence of bubbles or orange peel texture is the proportion of transparent contours. That is, by identifying the transparent contours in the highlight film through image recognition, the bubble or orange peel texture in the highlight film will produce a different color than other locations, thus creating a boundary line, which forms a contour. However, this contour is different from the white dots. The white dots are opaque, while this is transparent, thus forming the characteristic parameter for bubbles or orange peel texture. Its proportion has a lower limit, which can be set by empirical data.

[0111] Therefore, they are used as parameter-guided sets for each type of activation defect;

[0112] When adjusting the parameters for activation treatment, it is necessary to control the magnitude of parameter changes as much as possible, because excessive parameter increases will increase energy consumption. Taking the existing defect of white spots as an example, the cause is that the heating temperature is too low, the coating speed is too fast, and the pressure is insufficient. Therefore, the current temperature, coating speed, and pressure should be recorded, and the temperature, coating speed, and pressure should be increased until there are no white spots. The increased temperature, coating speed, and pressure should be recorded. The average temperature, coating speed, and pressure of the current temperature, coating speed, and pressure and the increased temperature, coating speed, and pressure should be taken. If no white spots are found under the conditions of temperature, lamination speed, and pressure, the average temperature, lamination speed, and pressure are updated. The updated values ​​are the average temperature, lamination speed, and pressure plus the average of the current temperature, lamination speed, and pressure. If white spots appear under the conditions of average temperature, lamination speed, and pressure, the values ​​of the average temperature, lamination speed, and pressure before the update are used as the target temperature, lamination speed, and pressure, respectively, and the processing is performed using the target temperature, lamination speed, and pressure. The same processing is applied to other conditions such as poor gloss, presence of white spots, and presence of bubbles or orange peel texture.

[0113] However, in reality, multiple defect types may coexist. Therefore, the parameters that need to be adjusted may overlap. Taking white spots and poor adhesion as examples, the parameters to be adjusted are temperature, lamination speed, and pressure. For temperature, the larger value of the treatment temperature for white spots and the treatment temperature for poor adhesion is used. The same approach is taken for lamination speed and pressure, which can complete the treatment. When more activation defect types coexist, a similar approach is taken. That is, when the treatment parameters for different activation defect types overlap, the largest value of the treatment parameters is used as the final parameter, which can meet the treatment requirements and control the range of parameters.

[0114] Reference Figure 2 As shown, constructing the parameter-guided set for activation defect types includes the following steps:

[0115] Each time an activation defect type occurs, the characterization parameters that exceed the normal range of the characterization parameters are summarized into a first guide preparatory set.

[0116] When the activation defect type does not appear, the characterization parameters that are outside the normal range of the characterization parameters are summarized into the second guide preparatory set;

[0117] By taking the intersection of at least one first pre-guiding set, a preliminary guiding set is obtained;

[0118] The parameterized set is obtained by taking the difference between the initial guiding set and the second guiding preparatory set.

[0119] The parameter-guided set for activation defect types mainly summarizes the characterization parameters that will cause data anomalies when an activation defect type is abnormal. Therefore, by comparing the target characterization parameters, the suspected activation defect types that may be abnormal can be identified, thereby narrowing down the scope of subsequent identification. However, since the abnormal characterization parameters identified each time during activation defect identification may not only be caused by a single activation defect type, but may also be caused by multiple activation defect types, it is difficult to determine the characterization parameters corresponding to the activation defect type. Here, by comparing the two cases of activation defect type presence and absence, the influence of other activation defect types can be investigated, and the characterization parameters corresponding to the activation defect type can be determined, thereby generating the parameter-guided set for activation defect types.

[0120] Reference Figure 3 As shown, the activation defect multidimensional fitting function for forming the parameter-guided set includes the following steps:

[0121] Based on historical detection data, the range of values ​​of the characterization parameters in the parameter-guided set that are outside the normal range is obtained. The range of values ​​of the characterization parameters in the parameter-guided set is divided into equal intervals to obtain at least one identification point. The identification point corresponds to the characterization parameter.

[0122] The representation parameters are randomly selected from the corresponding recognition points to form the first assignment scheme of the parameter-guided set;

[0123] The characterization parameters are all taken as fixed values ​​corresponding to the identification points, forming a second assignment scheme for the parameter-guided set;

[0124] The representation parameter is the sum of the values ​​of the representation parameter in the first assignment scheme and the values ​​of the representation parameter in the second assignment scheme, forming the third assignment scheme;

[0125] Under the condition that the parameter-guided set adopts the third assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the first anomaly degree;

[0126] Under the condition that the parameter-guided set adopts the second assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the second anomaly degree;

[0127] The third degree of abnormality is obtained by subtracting the first degree of abnormality from the second degree of abnormality.

[0128] The values ​​of the characterization parameters in the first assignment scheme are paired and fitted with the third degree of anomaly to obtain a multidimensional fitting function for activation defects, where the values ​​of the characterization parameters in the first assignment scheme are independent variables and the third degree of anomaly is the dependent variable.

[0129] Here, since the characterization parameter is a comprehensive result of multiple activation defects, separating the influence of other activation defects is extremely difficult. This is because it is necessary to accurately determine the anomalous degree of the remaining activation defects and, based on this anomalous degree, determine their influence value in the characterization parameter. The characterization parameter is then corrected to obtain a value affected only by a single activation defect type. Therefore, a third assignment scheme and a second assignment scheme are set. The difference between the corresponding characterization parameter values ​​in the third and second assignment schemes is exactly equal to the corresponding characterization parameter value in the first assignment scheme. Thus, the third anomalous degree, obtained by subtracting the first and second anomalous degrees, corresponds to the characterization parameter value in the first assignment scheme. Under the condition that the parameter-guided set adopts the third assignment scheme, all anomalous degrees of activation defect types are acquired. During multiple acquisitions, the remaining activation defects... The sum of abnormalities of defect types can be considered fixed. Under the condition of adopting the second assignment scheme for the parameter-guided set, all abnormalities of activation defect types are acquired. When acquired multiple times, the sum of abnormalities of other activation defect types can also be considered fixed. The two fixed cases are basically consistent. Therefore, the impact of abnormalities of other activation defect types on the parameter-guided set is consistent. Thus, the difference between the values ​​of the characterization parameters corresponding to the third assignment scheme and the second assignment scheme can eliminate the impact of abnormalities of other activation defect types on the parameter-guided set. As a result, the value of the characterization parameter in the first assignment scheme is only affected by the current single activation defect type. Therefore, the abnormality of the activation defect type corresponding to the parameter-guided set can be characterized by the activation defect multidimensional fitting function.

[0130] Based on the target characterization parameters and parameter-guided sets, obtaining at least one suspected activation defect type includes the following steps:

[0131] If all the representation parameters in the parameter-guided set are target representation parameters, then the parameter-guided set is taken as the target parameter-guided set; otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-guided set is taken as the suspected activation defect type.

[0132] Since the target characterization parameter is an abnormal parameter caused by the occurrence of activation defect type, but the abnormality of the target characterization parameter may be caused by other activation defect types, rather than by this activation defect type, it can only be regarded as a suspected activation defect type and further determination is required in the future.

[0133] Reference Figure 4 As shown, forming at least one combination of activation defects to be verified based on the suspected activation defect type includes the following steps:

[0134] Obtain all possible combinations of at least one suspected activation defect type, and treat them as combinations of suspected activation defect types.

[0135] The set of parameters corresponding to the suspected activation defect types in the suspected activation defect type combination is combined and then the set of guidance targets is obtained by taking the union of the sets of parameters.

[0136] When the set of guiding targets contains all the target characterization parameters, the suspected activation defect type combination is taken as the activation defect combination to be verified; otherwise, no processing is performed.

[0137] For suspected activation defect types, the actual activation defect type that appears may be one of all possible combinations. In order to identify each case, it is necessary to pre-form a combination of activation defects to be verified. However, the guiding target set must contain exactly all target characterization parameters. Otherwise, there will be inconsistencies between the guiding target set and all target characterization parameters, which can be categorized into two situations. The first situation is that the guiding target set contains characterization parameters that are different from all target characterization parameters, denoted as primary characterization parameters. This is inconsistent with the actual inspection situation because all target characterization parameters contain all abnormal characterization parameters, but primary characterization parameters are also abnormal parameters, but they are not included. The second situation is that all target characterization parameters contain parameters that are different from elements in the guiding target set, denoted as primary target characterization parameters. However, once a primary target characterization parameter appears, it must be caused by an activation defect type in the suspected activation defect type combination. When the guiding target set of the suspected activation defect type combination does not contain primary target characterization parameters, it means that there is no activation defect type that caused the primary target characterization parameters. Therefore, it does not meet the requirements.

[0138] Reference Figure 5 As shown, the decomposition of the actual values ​​of the target characterization parameters based on the suspected activation defect types in the activation defect combination to be verified includes the following steps:

[0139] The portion of the actual value of the target characterization parameter that exceeds the normal range is used as the feature value;

[0140] Obtain the anomaly range of the suspected activation defect type, divide the anomaly range of the suspected activation defect type at equal intervals, and obtain at least one test point;

[0141] The abnormality of suspected activation defect types is randomly selected from test points, and each selection method is used as an abnormal selection scheme for the combination of activation defects to be verified.

[0142] A proportionality coefficient is formed for suspected activation defect types, and based on the proportionality coefficient, a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is formed;

[0143] The degree of anomalousness of the suspected activation defect type in the eigenvalue and anomalous value scheme is multiplied by the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter to obtain the excess characterization value of the target characterization parameter of the suspected activation defect type. The excess characterization value of the target characterization parameter corresponds to the anomalous value scheme.

[0144] During decomposition, it is necessary to decompose based on the degree of anomaly of the suspected activation defect types. First, the proportional coefficient of the suspected activation defect type is determined to characterize its influence. Additionally, decomposition coefficients are formed relative to the target characterization parameter for each suspected activation defect type. Since the target characterization parameter is not affected by all suspected activation defect types, the decomposition coefficients are only determined for the relevant suspected activation defect types. This allows for the determination of any excess values ​​of the target characterization parameter. It is important to note that since the degree of anomaly of the suspected activation defect types is unknown, all anomaly value schemes need to be pre-formed for identification. In each anomaly value scheme, the degree of anomaly of the suspected activation defect type is determined. Subsequently, based on deduction, the required combination is selected.

[0145] Reference Figure 6 As shown, the proportion coefficient for forming a suspected activation defect type includes the following steps:

[0146] The intersection of the abnormal ranges of all suspected activation defect types is taken to obtain the feature range, and at least one feature point is uniformly selected in the feature range;

[0147] The values ​​of the feature points are substituted into the multidimensional fitting function of the activation defect corresponding to the suspected activation defect type, which serves as the constraint condition for the parameter-guided set corresponding to the suspected activation defect type.

[0148] All suspected activation defect types other than those whose proportional coefficients are to be determined are denoted as characteristic suspected activation defect types.

[0149] By combining the constraint conditions for suspected activation defect types with the constraint conditions for characteristic suspected activation defect types, at least one set of constraint conditions for suspected activation defect types can be obtained.

[0150] Under the simultaneous constraints of suspected activation defect types, the characterization parameters in the parameter-guided set corresponding to the suspected activation defect types are integrated to obtain the first integral value;

[0151] The second integral value is obtained by summing at least one first integral value for at least one feature point;

[0152] The second integral values ​​of all suspected activation defect types are summed to obtain the third integral value;

[0153] By comparing the second integral value with the third integral value, a proportionality coefficient for suspected activation defect types can be obtained.

[0154] The calculation here relies on the simultaneous solution of constraints on the suspected activation defect type and other suspected activation defect types. In essence, it is an equation with two multivariate unknowns, which restricts the values ​​of the unknowns. By restricting these values, the integral value under the simultaneous constraints can be obtained. By combining multiple integral values, the relative relationship between the suspected activation defect type and other suspected activation defect types can be obtained more reliably.

[0155] Reference Figure 7 As shown, the decomposition coefficients of the suspected activation defect type relative to the target characterization parameter include the following steps:

[0156] When the parameter-guided set of the suspected activation defect type contains the target characterization parameter, the suspected activation defect type is taken as the target suspected activation defect type. The proportion coefficient of the target suspected activation defect type is accumulated to obtain the comprehensive coefficient. The proportion coefficient of the target suspected activation defect type is divided by the comprehensive coefficient to obtain the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter.

[0157] When the parameter-guided set of the suspected activation defect type does not contain the target characterization parameter, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

[0158] Reference Figure 8 As shown, the calculation of the gap coefficient of the activation defect combination to be verified, based on the decomposition results of the actual values ​​of the target characterization parameters, includes the following steps:

[0159] Substitute the excess values ​​of the target characterization parameters of the suspected activation defect type in the anomaly sampling scheme into the corresponding activation defect multidimensional fitting function to obtain the predicted anomaly degree of the suspected activation defect type.

[0160] The variance between the abnormality degree of the suspected activation defect type and the predicted abnormality degree of the suspected activation defect type in the abnormal value scheme is calculated to obtain the deviation coefficient of the abnormal value scheme of the activation defect combination to be verified.

[0161] The minimum deviation coefficient of the abnormal value scheme of the activation defect combination to be verified is taken as the difference coefficient of the activation defect combination to be verified.

[0162] The selection criteria are as follows: when setting an anomalous value scheme in the combination of activation defects to be verified, the anomalousness of the combination of activation defects to be verified is assumed to be certain. However, based on the actual values ​​of the target characterization parameters, the degree of anomalousness can be predicted again according to the degree of anomalousness. Therefore, the predicted degree of anomalousness must be as small as possible from the set anomalous value scheme in the combination of activation defects to be verified. If it is very large, it means that the hypothesis is problematic. The possibility that the problematic situation is the actual situation is extremely small. Therefore, the situation with the smallest difference between the hypothesis and the prediction is selected as the actual situation. Thus, the reliability of the obtained results is high.

[0163] Reference Figure 9 As shown, the treatment of PET high-gloss film, based on the degree of abnormality of the actual activation defect type, includes the following steps:

[0164] The combination of activation defects to be verified that produces the actual activation defect type is taken as the target activation defect combination to be verified, and the abnormal value scheme of the target activation defect combination with the smallest deviation coefficient is taken as the target abnormal value scheme.

[0165] The value of the degree of abnormality of the actual activation defect type in the target abnormal value scheme is taken as the degree of abnormality of the actual activation defect type.

[0166] The abnormality of the actual activation defect type is divided by the baseline value to obtain the treatment coefficient. The treatment coefficient is multiplied by the parameters in the treatment scheme corresponding to the actual activation defect type and then summed to obtain the treatment correction scheme.

[0167] The treatment and correction schemes for all actual activation defect types are summarized to obtain the actual treatment scheme, and the PET high-gloss film is treated according to the actual treatment scheme.

[0168] Since the parameters in the processing scheme are based on the scenario where the degree of abnormality of the activation defect type is equal to the baseline value, they need to be modified to obtain a modified processing scheme. Only by using the modified processing scheme can the actual activation defect type be processed.

[0169] Furthermore, this solution also proposes a storage medium storing a computer-readable program, which, when invoked, executes the aforementioned online surface activation treatment method for biaxially oriented PET high-gloss film.

[0170] It is understandable that the storage medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid-state drive (SSD).

[0171] In summary, the advantages of this invention are as follows: by constructing a parameter-guided set of activation defect types, forming a multidimensional fitting function for activation defects of the parameter-guided set, decomposing the actual values ​​of the target characterization parameters and calculating the difference coefficient of the combination of activation defects to be verified, determining the suspected activation defect type based on the actual values ​​of the obtained characterization parameters, and predicting the activation defect situation based on this, and by comparing the predictions of various situations, selecting the activation defect situation that best matches the actual situation from various situations, thus determining both the type and degree of activation defect, thereby enabling targeted activation treatment based on the detection results of activation defects.

[0172] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for online surface activation treatment of biaxially oriented PET high-gloss film, characterized in that, include: Obtain at least one activation defect type, each activation defect type contains only one activation defect cause, and pre-obtain a processing plan based on the abnormality level of the activation defect type as a baseline value; Obtain at least one characterization parameter of the activation status of the PET high-gloss film, and obtain the normal range of the characterization parameter when the activation status of the PET high-gloss film is normal; A parameter-guided set of activation defect types is constructed. The parameter-guided set consists of characterization parameters, forming a multidimensional fitting function for activation defects of the parameter-guided set. The activation defect model is obtained by summarizing the parameter-guided set and its corresponding multidimensional fitting function for activation defects. The actual activation status of the PET high-gloss film surface was inspected to obtain the actual values ​​of the characterization parameters. The representation parameter whose actual value exceeds the corresponding normal range is used as the target representation parameter; Based on the target characterization parameters and parameter-guided set, at least one suspected activation defect type is obtained; Based on the suspected activation defect type, at least one combination of activation defects to be verified is formed; Based on the suspected activation defect types in the activation defect combination to be verified, the actual values ​​of the target characterization parameters are decomposed. Based on the decomposition results of the actual values ​​of the target characterization parameters, the gap coefficient of the activation defect combination to be verified is calculated. The activation defect type in the combination of activation defects to be verified with the smallest difference coefficient is taken as the actual activation defect type, and the PET high-gloss film is processed according to the degree of abnormality of the actual activation defect type. The activation defect multidimensional fitting function for forming the parameter-guided set includes the following steps: Based on historical detection data, the range of values ​​of the characterization parameters in the parameter-guided set that are outside the normal range is obtained. The range of values ​​of the characterization parameters in the parameter-guided set is divided into equal intervals to obtain at least one identification point. The identification point corresponds to the characterization parameter. The representation parameters are randomly selected from the corresponding recognition points to form the first assignment scheme of the parameter-guided set; The characterization parameters are all taken as fixed values ​​corresponding to the identification points, forming a second assignment scheme for the parameter-guided set; The representation parameter is the sum of the values ​​of the representation parameter in the first assignment scheme and the values ​​of the representation parameter in the second assignment scheme, forming the third assignment scheme; Under the condition that the parameter-guided set adopts the third assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the first anomaly degree; Under the condition that the parameter-guided set adopts the second assignment scheme, the average value of all anomalies of the activation defect type is taken to obtain the second anomaly degree; The third degree of abnormality is obtained by subtracting the first degree of abnormality from the second degree of abnormality. The values ​​of the characterization parameters in the first assignment scheme are paired and fitted with the third degree of anomaly to obtain a multidimensional fitting function for activation defects, wherein the values ​​of the characterization parameters in the first assignment scheme are independent variables and the third degree of anomaly is a dependent variable. The decomposition of the actual values ​​of the target characterization parameters based on the suspected activation defect types in the activation defect combination to be verified includes the following steps: The portion of the actual value of the target characterization parameter that exceeds the normal range is used as the feature value; Obtain the anomaly range of the suspected activation defect type, divide the anomaly range of the suspected activation defect type at equal intervals, and obtain at least one test point; The abnormality of suspected activation defect types is randomly selected from test points, and each selection method is used as an abnormal selection scheme for the combination of activation defects to be verified. A proportionality coefficient is formed for suspected activation defect types, and based on the proportionality coefficient, a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is formed; The degree of anomalousness of the suspected activation defect type in the eigenvalue and anomalous value scheme is multiplied by the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter to obtain the excess characterization value of the target characterization parameter of the suspected activation defect type. The excess characterization value of the target characterization parameter corresponds to the anomalous value scheme. The calculation of the gap coefficient of the activation defect combination to be verified based on the decomposition results of the actual values ​​of the target characterization parameters includes the following steps: Substitute the excess values ​​of the target characterization parameters of the suspected activation defect type in the anomaly sampling scheme into the corresponding activation defect multidimensional fitting function to obtain the predicted anomaly degree of the suspected activation defect type. The variance between the abnormality degree of the suspected activation defect type and the predicted abnormality degree of the suspected activation defect type in the abnormal value scheme is calculated to obtain the deviation coefficient of the abnormal value scheme of the activation defect combination to be verified. The minimum deviation coefficient of the abnormal value scheme of the activation defect combination to be verified is taken as the difference coefficient of the activation defect combination to be verified.

2. The method for online surface activation treatment of biaxially oriented PET high-gloss film according to claim 1, characterized in that, The process of constructing the parameter-guided set for activation defect types includes the following steps: Each time an activation defect type occurs, the characterization parameters that exceed the normal range of the characterization parameters are summarized into a first guide preparatory set. When the activation defect type does not appear, the characterization parameters that are outside the normal range of the characterization parameters are summarized into the second guide preparatory set; By taking the intersection of at least one first pre-guiding set, a preliminary guiding set is obtained; The parameterized set is obtained by taking the difference between the initial guiding set and the second guiding preparatory set.

3. The online surface activation treatment method for biaxially oriented PET high-gloss film according to claim 2, characterized in that, The process of obtaining at least one suspected activation defect type based on the target characterization parameters and parameter-guided set includes the following steps: If all the representation parameters in the parameter-guided set are target representation parameters, then the parameter-guided set is taken as the target parameter-guided set; otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-guided set is taken as the suspected activation defect type.

4. The online surface activation treatment method for biaxially oriented PET high-gloss film according to claim 3, characterized in that, The process of forming at least one combination of activation defects to be verified based on suspected activation defect types includes the following steps: Obtain all possible combinations of at least one suspected activation defect type, and treat them as combinations of suspected activation defect types. The set of parameters corresponding to the suspected activation defect types in the suspected activation defect type combination is combined and then the set of guidance targets is obtained by taking the union of the sets of parameters. When the set of guiding targets contains all the target characterization parameters, the suspected activation defect type combination is taken as the activation defect combination to be verified; otherwise, no processing is performed.

5. The online surface activation treatment method for biaxially oriented PET high-gloss film according to claim 4, characterized in that, The proportion coefficient for forming suspected activation defect types includes the following steps: The intersection of the abnormal ranges of all suspected activation defect types is taken to obtain the feature range, and at least one feature point is uniformly selected in the feature range; The values ​​of the feature points are substituted into the multidimensional fitting function of the activation defect corresponding to the suspected activation defect type, which serves as the constraint condition for the parameter-guided set corresponding to the suspected activation defect type. All suspected activation defect types other than those whose proportional coefficients are to be determined are denoted as characteristic suspected activation defect types. By combining the constraint conditions for suspected activation defect types with the constraint conditions for characteristic suspected activation defect types, at least one set of constraint conditions for suspected activation defect types can be obtained. Under the simultaneous constraints of suspected activation defect types, the characterization parameters in the parameter-guided set corresponding to the suspected activation defect types are integrated to obtain the first integral value; The second integral value is obtained by summing at least one first integral value for at least one feature point; The second integral values ​​of all suspected activation defect types are summed to obtain the third integral value; By comparing the second integral value with the third integral value, a proportionality coefficient for suspected activation defect types can be obtained.

6. The online surface activation treatment method for biaxially oriented PET high-gloss film according to claim 5, characterized in that, The process of forming the decomposition coefficients of the suspected activation defect type relative to the target characterization parameter includes the following steps: When the parameter-guided set of the suspected activation defect type contains the target characterization parameter, the suspected activation defect type is taken as the target suspected activation defect type. The proportion coefficient of the target suspected activation defect type is accumulated to obtain the comprehensive coefficient. The proportion coefficient of the target suspected activation defect type is divided by the comprehensive coefficient to obtain the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter. When the parameter-guided set of the suspected activation defect type does not contain the target characterization parameter, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

7. The online surface activation treatment method for biaxially oriented PET high-gloss film according to claim 6, characterized in that, The process of treating the PET high-gloss film according to the degree of abnormality of the actual activation defect type includes the following steps: The combination of activation defects to be verified that produces the actual activation defect type is taken as the target activation defect combination to be verified, and the abnormal value scheme of the target activation defect combination with the smallest deviation coefficient is taken as the target abnormal value scheme. The value of the degree of abnormality of the actual activation defect type in the target abnormal value scheme is taken as the degree of abnormality of the actual activation defect type. The abnormality of the actual activation defect type is divided by the baseline value to obtain the treatment coefficient. The treatment coefficient is multiplied by the parameters in the treatment scheme corresponding to the actual activation defect type and then summed to obtain the treatment correction scheme. The treatment and correction schemes for all actual activation defect types are summarized to obtain the actual treatment scheme, and the PET high-gloss film is treated according to the actual treatment scheme.

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